HGrandom2

退火粒子群优化算法（SAPSO），matlab代码，仅供学习(Annealing particle swarm optimization (SAPSO), matlab code, only to learn)

allencahn方程的matlab实现，附图(allencahn in matlab code)

使用matlab编写的：用神经网络方法来辨识质心侧偏角程序。供大家使用参考。(Prepared using matlab: A neural network method to identify centroid lateral declination procedures. For everyone to use reference.)

Pixel classification code using K-Nearest Neighbor Classifier.Here three pixel values of color image are taken as training sample.Color based segmented image was used to classify the object into three classes.

说明：  异步电动机有速度传感器矢量控制，逆变侧使用SVPWM，启动超调量约3.2%(The asynchronous motor has speed sensor vector control, the inverter side uses SVPWM, and the starting overshoot is about 3.2%)

这是我们的一次作业，图像添加水印，图像翻转等，希望有用(This is our first operation, image watermark, image flip in the hope that a useful)

说明：  图像潜载信息映射技术,基于matlab基础知道编程使之能够在公开图中潜载隐藏另一幅图象。(the technology about translating the image embeded )

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.

田纳西伊斯曼过程SIMULINK仿真模块 来自于经典文献(Tennessee Eastman process SIMULINK simulation module from the classic literature)

超松弛迭代法编写matlab程序求解槽空间电位分布问题(Overrelaxation iterative method to write matlab program for solving groove space potential distribution problems)